docs: def get_depth_pmf(self, depth_bins, default_depth=5.0, bootstrap=None):
          """
          Returns the depth distribution of the catalogue as a probability mass
          function
          """
          if len(self.data['depth']) == 0:
              # If depth information is missing
              return PMF([(1.0, default_depth)])
          # Get the depth distribution
          depth_hist = self.get_depth_distribution(depth_bins,
                                                   normalisation=True,
                                                   bootstrap=bootstrap)
          # If the histogram does not sum to 1.0 then remove the difference
          # from the lowest bin
          depth_hist = np.around(depth_hist, 3)
          while depth_hist.sum() - 1.0:
              depth_hist[-1] -= depth_hist.sum() - 1.0
              depth_hist = np.around(depth_hist, 3)
  
          pmf_list = []
          for iloc, prob in enumerate(depth_hist):
              pmf_list.append((prob,
                               (depth_bins[iloc] + depth_bins[iloc + 1]) / 2.0))
          return PMF(pmf_list)